1. Field of the Invention
This invention relates generally to the field of geophysical prospecting. More particularly, the invention relates to the field of seismic data processing. Specifically, the invention is a method of correcting for time shifts in seismic data resulting from azimuthal variation.
2. Description of the Related Art
Current seismic data acquisition leads to seismic data being irregularly sampled along the spatial coordinates. For conventional seismic data sets, these coordinates are typically the in-line midpoint, cross-line midpoint, offset and azimuth. This irregular sampling can generate problems for time-lapse seismic and imaging, including pre-stack imaging. The irregular sampling in midpoints and offset can be regularized using conventional regularization or reconstruction techniques, such as Fourier regularization. These regularization techniques calculate or estimate new values for the in-line midpoint, cross-line midpoint, and offset variables so that these variables are regularly sampled. However, these techniques, including Fourier regularization, do not compensate for variation in the azimuth variable. Nonetheless, azimuthal variations can have a large influence on the processing of seismic data.
If a single dipping layer in a homogeneous subsurface is considered, and two traces are compared with the same midpoint and absolute offset, but different azimuths, then the reflection event will shift in time. The time shift is dependent on the dip-angle and direction of the layer, the velocity in the subsurface, the azimuth, and the offset. These time shifts due to azimuth variations are limited, typically in the order of a few milliseconds. Thus, for general seismic data imaging needs, neglecting these time shifts will have limited consequences. However, for time-lapse data, where base and monitor surveys are differenced, even a time shift of 4 ms can lead to errors of the same magnitude as the difference in the measured signals. For steeply dipping layers, in particular with oblique dip-directions, neglecting azimuth variation is not an effective approach for time-lapse data. Here, repeatability is essential.
After Fourier regularization, the in-line and cross-line midpoints and absolute offsets in the regularized traces are regularly sampled. For several further processing methods, such as dip-moveout correction and prestack migration, the source and receiver coordinates are needed. To derive these positions from the midpoint and offset positions, an azimuth is needed. One approach is to assume that the azimuth is zero relative to the in-line direction. This is the sailing direction in a marine seismic survey. However, assuming a zero azimuth relative to the sailing direction is not ideal, because the seismic signal will depend on the azimuth. In principle, if the azimuth is changed, the data should be corrected for this particular change in azimuth. Another approach is to assume that each new regularized trace has almost the same azimuth as the closest input trace. Estimating azimuths by the closest input trace is physically more correct than assuming a zero azimuth relative to the sailing direction. However, this azimuth estimation approach can lead to problems in further processing algorithms. For example, it is desirable for dip-moveout correction and prestack migration to have data that is regularly sampled in midpoint, offset and azimuth. This is discussed by Canning, A. and Gardner, G. H. F., 1996, “Another look at the question of azimuth:” The Leading Edge, 15, no. 07, 821-823.
Duijndam, A. J. W. et al., 1999, “A general reconstruction scheme for dominant azimuth 3D seismic data”, 69th Ann. Internat. Mtg: Soc. of Expl. Geophys., Expanded Abstracts, describe a method for reconstructing (regularizing) irregularly sampled seismic data, employing Fourier or Radon regularization. Their reconstruction scheme comprises reposting the data along receiver lines to exact cross-line positions, followed by least squares reconstruction in the midpoint-offset domain along cross-lines and after NMO correction. They assume the case of seismic data acquisition with a predominant azimuth for long offsets. However, they ignore azimuth variation.
Duijndam, A. J. W. et al., point out that an effective regularization scheme would have a number of beneficial applications in seismic data processing. It could improve the generation of pseudo zero-offset data for conventional binstack techniques. It could regularize and generate missing data for prestack processing that requires dense and regular sampling, such as three-dimensional prestack imaging and three-dimensional surface related multiple attenuation. It could improve the match of time-lapse seismic data and improve amplitude versus angle (AVA) analysis. It could improve coherent noise attenuation on prestack data, allowing high-resolution migration of single common offset data volumes.
Thus, a need exists for a regularization method for irregularly sampled seismic data that provides corrections for the time shifts due to azimuth variations. This will improve the repeatability of time-lapse seismic data sampling and processing.